Real-time facial action unit intensity prediction with regularized metric learning
نویسندگان
چکیده
منابع مشابه
Personalized Modeling of Facial Action Unit Intensity
Facial expressions depend greatly on facial morphology and expressiveness of the observed person. Recent studies have shown great improvement of the personalized over non-personalized models in variety of facial expression related tasks, such as face and emotion recognition. However, in the context of facial action unit (AU) intensity estimation, personalized modeling has been scarcely investig...
متن کاملOutput Regularized Metric Learning with Side Information
Distance metric learning has been widely investigated in machine learning and information retrieval. In this paper, we study a particular content-based image retrieval application of learning distance metrics from historical relevance feedback log data, which leads to a novel scenario called collaborative image retrieval. The log data provide the side information expressed as relevance judgemen...
متن کاملUpper Facial Action Unit Recognition
This paper concentrates on the comparisons of systems that are used for the recognition of expressions generated by six upper face action units (AU s) by using Facial Action Coding System (FACS). Haar wavelet, Haar-Like and Gabor wavelet coe cients are compared, using Adaboost for feature selection. The binary classi cation results by using Support Vector Machines (SVM ) for the upper face AU s...
متن کاملRegularized Distance Metric Learning: Theory and Algorithm
In this paper, we examine the generalization error of regularized distance metric learning. We show that with appropriate constraints, the generalization error of regularized distance metric learning could be independent from the dimensionality, making it suitable for handling high dimensional data. In addition, we present an efficient online learning algorithm for regularized distance metric l...
متن کاملManifold Regularized Transfer Distance Metric Learning
The performance of many computer vision and machine learning algorithms are heavily depend on the distance metric between samples. It is necessary to e xploit abundant of side information like pairwise constraints to learn a robust and reliable distance metric. While in real world application, large quantities of labeled data is unavailable due to the high labeling cost. Transfer distance metri...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Image and Vision Computing
سال: 2016
ISSN: 0262-8856
DOI: 10.1016/j.imavis.2016.03.004